Forecasting of CO2 Emissions, Renewable Energy Consumption and Economic Growth in Vietnam Using Grey Models

Ho, Hong-Xuyen Thi

doi:10.1109/gtsd.2018.8595679

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…The results of this study are also in line with research conducted by Baffoe-Bonnie and Mensah (2018), Vo et al (2019), Islam et al (2017), G.C. andAdhikari (2021), Thongrawd and Kerdpitak (2020), Ho (2018), Arista and Amar (2019), and Tang (2017) which state that economic growth has a relationship and positively affects CO 2 emissions.…”

Section: Determinants Of Co2 Emission: Evidence From United Statessupporting

confidence: 91%

Determinants of CO2 Emission: Evidence from United States

A'yun,

Anggrayni

2023

JERSS

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The decline in environmental quality can be marked by an increase in the earth's temperature or what we call global warming. Global warming can be caused by increasing concentrations of greenhouse gas (GHG) emissions on earth, one of the most important components of which is CO2 emissions. This study aims to determine the effect of economic growth, energy consumption, forest area and urbanization on CO2 emissions in United States in the period 1970-2020. This study uses a Vector Error Correction Model (VECM) analysis model. The results of the study indicate that economic growth, energy consumption, and foreign direct investment have a significant effect on CO2 emissions in the long term.

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Section: Determinants Of Co2 Emission: Evidence From United Statessupporting

confidence: 91%

Determinants of CO2 Emission: Evidence from United States

A'yun,

Anggrayni

2023

JERSS

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“…Grey System Theory (GST) was proposed in the 1980s to deal with the uncertainty caused by incomplete information and VOLUME 8, 2020 inaccurate data. The GST has been successfully applied in the areas of environment [28], military [29], business [30], agriculture [31], geology [32], and meteorology [33].…”

Section: A Grey System Theorymentioning

confidence: 99%

Enhance the Uncertainty Modeling Ability of Fuzzy Grey Cognitive Maps by General Grey Number

2020

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In real-life systems, people cannot get precise data. The data are represented in the forms of interval or multiple intervals in many cases. Most intelligent algorithms are designed for precise data in algorithm research. People always use a real number contained in the interval or multiple intervals as the candidate for the precise data. However, such a measure will lose lots of information contained in the interval or multiple intervals. Fuzzy Cognitive Map (FCM) is one of the famous intelligent algorithms. The Fuzzy Grey Cognitive Map (FGCM) was proposed to enable FCM to do interval computation. But FGCM can only deal with a single interval, as for multiple intervals, the FGCM is powerless. Thus, this paper aims to enhance the FGCM to make it can cope with multiple intervals. The paper introduces the general grey number and deduces the new activation functions according to Grey System Theory (GST) and Taylor series. Finally, an industrial process control problem is applied to verify the new algorithm. The results show that the new algorithm is not only compatible with the original FGCM and FCM, but can process more uncertain knowledge and data. In general, the new algorithm inherits most of FGCM's characteristics and can cope with the data expressed by multiple intervals, which means it can be used in environments with more uncertain knowledge and data.INDEX TERMS Fuzzy grey cognitive map, fuzzy cognitive map, uncertainty modeling, general grey number, grey system theory.

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“…Other approaches involve the assessment of other specific criteria, for example: Renewable energy fraction and electricity sold (Rad et al, 2020), the volume of energy produced (Karmaker et al, 2018), total energy consumption (Ho, 2018;Nieves et al, 2019;Rentizelas et al, 2019), total CO 2 emissions [34,38,39,46,50].…”

Section: Literature Review Of Methods For Assessing the Effectiveness...mentioning

confidence: 99%

Future Development of Renewable Energy in Russia: A Case of Solar Power

et al. 2022

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Currently, there is a growing number of studies related to assessing the cost-effectiveness of renewable energy projects around the world. This topic is also very relevant for the Russian energy market that is otherwise dominated by oil and gas. The Russian Ministry of Energy forecasts that local power plants running on the renewable energy sources (RES) may not become economically efficient until 2025 or later. However, a research question arises: Is it possible to effectively implement RES projects in Russia without any state support? In order to answer this question, the authors need to assess the economic feasibility of seven scenarios for the construction of a solar power plant in the Orenburg region of Russia. The methodological basis of this work is the widely used in the energy sector classic method of investment analysis based on the calculation of discounted indicators: net present value (NPV), payback period (DPP) and internal rate of return (IRR) of the project. All our calculations are based on industry-specific initial capital investment estimates, energy storage equipment costs, and related annual operating costs. This led to the development of the scenarios on the basis of an analysis of the features of electricity and capacity generation in the Orenburg region, the existing options for joining the trading system of the energy market, energy storage applications, as well as the availability of the Russian government support and funding for the solar energy projects. Our results demonstrate that the economic feasibility of the development of renewable energy in Russia can become a reality. Out of the seven scenarios, three yielded the positive economic outcome (among them there were the two project scenarios without government support). These two projects featured the sale of electricity in retail markets and the installation of an additional energy storage system, despite an almost twofold increase in capital investments. The projects in question achieved the best economic results according to the three calculated criteria. In particular, the scenario that provided for the work on the retail market directly to the energy consumer receives an NPV of more than 1.5 times higher than other projects, an IRR of 10% and pays off at least 3 years faster. Achieving a positive economic result in the wholesale market was possible only in case of state support for the project. In addition, this scenario of the project numerically reached almost identical indicators, as in the case of selling energy on the retail market in the region where the power facility operates: NPV is almost 127 million rubles, IRR is within 13.9%, and DPP is 15 years. Other scenarios that do not provide for the use of energy storage systems or do not have state support for working in the wholesale market are not self-sustaining even during the 25-year life of energy equipment. These results might have practical significance and will be used in developing an approach to creating a profile of regions in terms of the advancements of renewable energy, as well as in developing strategies for the incentives of this sector in Russia.

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Forecasting of CO2 Emissions, Renewable Energy Consumption and Economic Growth in Vietnam Using Grey Models

Cited by 12 publications

References 10 publications

Determinants of CO2 Emission: Evidence from United States

Determinants of CO2 Emission: Evidence from United States

Enhance the Uncertainty Modeling Ability of Fuzzy Grey Cognitive Maps by General Grey Number

Future Development of Renewable Energy in Russia: A Case of Solar Power

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